CN107126302B - Upper and lower limb movement simulation processing method - Google Patents

Upper and lower limb movement simulation processing method Download PDF

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CN107126302B
CN107126302B CN201710082333.7A CN201710082333A CN107126302B CN 107126302 B CN107126302 B CN 107126302B CN 201710082333 A CN201710082333 A CN 201710082333A CN 107126302 B CN107126302 B CN 107126302B
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王薇
雷硕
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Shanghai Shuli Intelligent Technology Co ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
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Abstract

The invention discloses a simulation processing method for upper and lower limb movement, which is characterized in that a Micro USB interface is connected with a surface electrode to collect a human body surface electromyographic signal, the surface electromyographic signal is collected, a series of algorithms such as denoising, filtering, spectrum analysis and normalization are carried out, an effective movement control signal is generated by using a mode recognition algorithm such as a neural network and the like, and the effective movement control signal is input to an upper and lower limb movement device controller through Bluetooth equipment, so that the aim of controlling a wearable artificial hand/arm/foot/leg by using the human body surface muscle signal is fulfilled.

Description

Upper and lower limb movement simulation processing method
Technical Field
The invention relates to the field of novel bionic artificial limb movement, in particular to a simulation processing method capable of realizing upper and lower limb movement.
Background
Prostheses are a necessity for amputees, and only a few of the patients with limb disabilities worldwide can afford to wear prostheses. Most of artificial limbs on the market at present are decorative artificial limbs, the appearance design of the artificial limbs is the same as that of real arms, but no function is provided; some artificial limbs try to control the movement of the artificial limbs through bioelectric current signals, and are expensive after being commercialized, single in gesture and difficult to bear by ordinary families.
In high-end amputation equipment, a device for controlling the upper and lower limbs to move by using a mobile phone as an algorithm arithmetic unit is not provided, the control system is developed only based on chips such as a single chip microcomputer, the chip arithmetic capability is limited, the myoelectric signal analysis precision is low, the real-time performance is poor, the universality is not realized, the development cost is high, and therefore, the artificial limb for sports is expensive and is not suitable for wide market popularization.
In addition, the bionic hand/arm/foot/leg controlled by myoelectricity or electroencephalogram has high cost, unstable algorithm and long operation time. Currently, the myoelectric solution on the market generally directly performs signal amplitude acquisition on a single muscle surface signal, and the signal amplitude acquisition is used as a command for triggering a designated movement by a muscle. The existing method is influenced by surrounding electromagnetic signals, low-frequency signals of human body movement, noise brought by a motor, signals of other muscle groups, deep muscle signals and white noise. Resulting in ambiguous movement instructions and subjectively conscious movements that cannot be accomplished qualitatively. In the current laboratory solution, in order to avoid the signal interference, signals are more complete and accurate, a large number of background algorithms such as wavelet analysis are operated, and thus, requirements of a large number of operations on the speed and the capability of a processor are high, although single signal processing precision is improved when the background algorithms are configured in the existing wearable artificial hand/arm/foot/leg, the cost is greatly improved, the operation stability is reduced on the contrary, and the background algorithms are only suitable for laboratory scientific research and are not suitable for wide market popularization.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a simulation processing method capable of realizing the motion of the upper and lower limbs.
The technical scheme is as follows: in order to solve the technical problem, the invention provides a simulation processing method for upper and lower limb movement, which comprises the following steps:
step 1) selecting relevant muscles or muscle groups for completing specific actions;
step 2) extracting surface muscle signals by using a surface electrode, wherein the sampling frequency of the signals needs to be more than 1000Hz, and removing NaN signal values in sampling;
step 3): performing primary processing on the signals, eliminating hollow signal values, filtering the signals, and eliminating white noise, environmental electromagnetic signals and low-frequency signals generated by the movement of limbs of the user;
step 4): analyzing a frequency domain spectrum, filtering, and converting back to a time domain one-dimensional signal, wherein the range of the normal muscle electric signal is 8-500 Hz;
step 5): fitting a change curve of the contraction intensity of a single muscle in a designated exercise, muscle reaction time, contraction time and decay time;
step 6): correlation analysis and curve fitting of the above 4 parameters of two or more groups of muscles in the same designated action;
step 7): simulation modeling is carried out on electromyographic signals of all muscle groups with specified actions and relevant time and strength relations;
step 8): storing and sending signals to an external controller;
step 9): the prosthetic hand/arm/foot/leg performs the specified action of the muscle output signal.
Further, the motion modes comprise fist making, unfolding, showing, OK and V-shaped gestures.
The step 3) comprises the following specific steps: a large amount of external environment and low-frequency signals generated by self movement are mixed in muscle signals extracted from the surface of skin through a surface electrode, the extracted surface muscle electrical signals are subjected to spectrum analysis, time domain one-dimensional digital signals are converted into frequency domains of the time domain one-dimensional digital signals, so that frequency spectrum information of signals generated by various signal sources is obtained, in the frequency domain signals of the frequency spectrum of 0Hz-500Hz, environmental noise (50Hz signals) is eliminated through a low-pass filter and a band-pass filter, the low-frequency signals (less than 2Hz signals) of movement and interference signals generated by deep muscles are removed, and the signal frequency spectrum finally used for simulation output falls between 8Hz-500Hz and is converted back to a time domain signal space.
Further, in the step 6), each designated action is completed through the cooperation of one or more groups of muscles, in the completion of the specific designated action, the initial reaction time, the contraction time and the decay time of each muscle of the muscle groups have specific changes and characteristics, and the correlation and the fitted curve of the muscle signals are obtained through extracting the relevant parameters, the reaction time, the contraction time and the decay time and through curve fitting and a signal correlation algorithm, so as to provide input parameters for the next simulation modeling.
Further, the simulation modeling process of step 7) is as follows:
the electrical muscle surface signals are composed of electrical impulses generated by a plurality of muscle motor unit groups, the electrical impulses generated by the motor unit groups are composed of countless electrical impulses of single muscle fibers in each unit group, wherein,
7.1) electric pulses V generated by the moving element groupsmui，
Figure BDA0001226096410000031
Wherein, VmuiCan be derived from equation 2; i is 1 to n, Vmuti is the number of electrical pulses generated by each single motion unit group, friIs the rate of motion unit release and follows a poisson distribution;
7.2) electricity generated by a single moving element groupPulse Vmu，
Figure BDA0001226096410000032
Wherein the content of the first and second substances,
Figure BDA0001226096410000034
is based on a single electrical pulse of muscle fibers in a given motor unit; n is a radical offIs the muscle fiber number;
7.3) the muscle contraction time of each group of motor units,
Figure BDA0001226096410000033
wherein N is 1,2mu；NmuIs based on the number of motor units a given muscle contains; fr is the median rate of release of the motor unit, and the default value is fr is 85 ms;
7.4) Single muscle fiber Electrical pulse V (x, y, z),
Figure BDA0001226096410000041
wherein the electrical impulses between muscle fiber cells ei(z)；
7.5) Electrical impulses between muscle fiber cells ei(z)，
ei(z)＝96z3e-z-90 (5)
Wherein ei(z) is the electrical impulse between muscle fiber cells; z is the axial distance in millimeters; s is the fiber cross-section; sigmaiIs intercellular conductivity; sigmamIs muscle conductivity; r is the distance from the cross section of the fiber to the observation point;
7.6) muscle conductivity σmMuscle axial conductivity σzAnd muscle radial conductivity σy
Figure BDA0001226096410000042
7.7) according to Green's law, the electrical impulses for a single muscle fiber can be derived as in equation (7),
Figure BDA0001226096410000043
wherein S1, S2 are the sections of both ends of the muscle fiber, S is the muscle fiber section;
Figure BDA0001226096410000044
where d is the fiber diameter, H1 has no meaning, but is a simplified formula to extract the coefficient.
Has the advantages that: compared with the prior art, the invention has the following advantages:
the invention provides a simulation processing method for upper and lower limb movement, which improves the accuracy of surface muscle electrical signal extraction and collection; environmental signals, peripheral interference signals and other non-participated muscle signals are eliminated cleanly; instructions for delivering a specific intensity and time of locomotor activity in combination with relative muscle group signals; the myoelectric signal is used for direct control, and the method has the advantages of high recognition rate, good real-time property, sensitive response, low cost, convenience and practicability.
Drawings
FIG. 1 is a schematic diagram of the selection of relevant muscles or muscle groups for a particular activity of the present invention.
Fig. 2 is a schematic diagram of the raw acquisition of electrical signals of surface muscles.
FIG. 3 is a schematic representation of a processed surface muscle signal.
FIG. 4 is a schematic illustration of the spectral analysis of the processed surface muscle signal.
FIG. 5 is a graph of the signal contraction time, single muscle response time and bilateral muscle strength based on the palm flexion and extension for a given motor.
FIG. 6 is a graph showing the time of contraction, response time and strength of muscles on both sides of a given motor signal based on the change in flexion and extension of the palm.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a simulation processing method for upper and lower limb movement, the method comprises the following steps:
step 1) selecting relevant muscles or muscle groups for completing specific actions; as shown in fig. 1, when the palm flexion and extension movements are performed, the muscle groups of the wrist are not selected for the user who installs the artificial hand and has only a small part of the upper arm left, and the extensor carpi ulnaris and flexor carpi radialis are used as the muscle groups for performing the designated movements.
Step 2) extracting surface muscle signals by using a surface electrode, wherein the sampling frequency of the signals needs to be more than 1000Hz, and removing NaN signal values in sampling; as shown in fig. 2, which is a raw signal curve, this step simply removes the null signals in the sampled signals, which are raw muscle signals extracted from two different muscle groups, the frequency of which is typically between 8Hz and 500Hz based on the surface muscle signals, where sampling frequencies greater than 1000Hz are used to sample the analog signals and remove the NaN null signals, resulting in the raw digital muscle signals shown in fig. 2.
Step 3): performing primary processing on the signals, eliminating hollow signal values, filtering the signals, and eliminating white noise, environmental electromagnetic signals and low-frequency signals generated by the movement of limbs of the user; a large amount of external environment and low-frequency signals generated by self movement are mixed in muscle signals extracted from the surface of skin through a surface electrode, the extracted surface muscle electrical signals are subjected to spectrum analysis, time domain one-dimensional digital signals are converted into frequency domains of the time domain one-dimensional digital signals, so that frequency spectrum information of signals generated by various signal sources is obtained, in the frequency domain signals of the frequency spectrum of 0Hz-500Hz, environmental noise (50Hz signals) is eliminated through a low-pass filter and a band-pass filter, the low-frequency signals (less than 2Hz signals) of movement and interference signals generated by deep muscles are removed, and the signal frequency spectrum finally used for simulation output falls between 8Hz-500Hz and is converted back to a time domain signal space.
Step 4): analyzing a frequency domain spectrum, filtering, and converting back to a time domain one-dimensional signal, wherein the range of the normal muscle electric signal is 8-500 Hz; as shown in fig. 4, the processed surface muscle signal spectrum analysis (dual channel/dual muscle group) analyzes the distribution of the energy of the signal with frequency through the Power Spectral Density (PSD).
Fig. 4 shows the frequency spectrum information of the surface muscle signals generated by the two muscle groups which complete the stretching and holding of the palm and the designated action, wherein the low-frequency signals generated by the self-movement of the limb and the 50Hz signals of the external electromagnetic noise are eliminated by adopting low-pass and band-pass filters, and the frequency of the surface muscle signals of the two muscle groups is mainly concentrated between 8Hz and 100Hz as can be seen from the frequency domain energy spectrum of fig. 4. It can be clearly seen that the high frequency signal of the muscle group 2 is significantly higher than the high frequency signal of the muscle group 1, and it is described from the side that the muscle group 2 mainly contributes to the control of the muscle posture, and the muscle group 1 mainly contributes to the exercise initiation and the muscle strength.
Step 5): fitting a change curve of the contraction intensity of a single muscle in a designated exercise, muscle reaction time, contraction time and decay time; as shown in fig. 3 and 5, fig. 3 shows the signal representation of the time domain space converted back from the surface muscle signals generated by the two groups of muscles when the palm stretching and holding and the designated action are completed through a series of signal processing algorithms such as frequency domain analysis, filtering, denoising and the like. Here the reaction time, contraction intensity, and decay time of the muscle can be seen, and the differentiation of muscle work time and intensity of two different groups of muscles at the same point in time can be seen. Fig. 5 shows the analysis of two sets of parameters characterizing the movement of muscle signals, where the response time, contraction intensity, and decay time are all plotted on the same time axis.
Step 6): correlation analysis and curve fitting of the above 4 parameters of two or more groups of muscles in the same designated action; as shown in fig. 6; fig. 6 shows the correlation of the characteristic parameters of the two muscle signal movements, and the correlation analysis and curve fitting are performed on the response time, contraction intensity, and decay time of the two muscle groups completing the same designated action, and the correlation operation is performed on the time deviation, intensity, and peak deviation of the start and decay of the muscle movement, so that the simulation modeling is performed to provide the output signal. The completion of each designated action is completed through the combined action of one or more groups of muscle groups, in the completion of the specific designated action, the initial reaction time, the contraction time and the decay time of each muscle of the muscle groups have specific changes and characteristics, and the correlation and the fitted curve of the muscle signals are obtained through extracting the relevant parameters, the reaction time, the contraction time and the decay time and through curve fitting and a signal correlation algorithm, so that input parameters are provided for the next step of simulation modeling.
Step 7): simulation modeling is carried out on electromyographic signals of all muscle groups with specified actions and relevant time and strength relations; as shown in fig. 6. The simulation modeling process is as follows:
the electrical muscle surface signals are composed of electrical impulses generated by a plurality of muscle motor unit groups, the electrical impulses generated by the motor unit groups are composed of countless electrical impulses of single muscle fibers in each unit group, wherein,
7.1) electric pulses V generated by the moving element groupsmui，
Figure BDA0001226096410000071
Wherein, VmuiCan be derived from equation 2; friIs the rate of motion unit release and follows a poisson distribution
7.2) electric pulses V generated by a single group of locomotor unitsmu，
Figure BDA0001226096410000072
Wherein the content of the first and second substances,
Figure BDA0001226096410000074
is based on a single electrical pulse of muscle fibers in a given motor unit; n is a radical offIs the muscle fiber number;
7.3) the muscle contraction time of each group of motor units,
Figure BDA0001226096410000073
wherein N is 1,2mu；NmuIs based on the number of motor units a given muscle contains; fr is the median rate of release of the motor unit, and the default value is fr is 85 ms;
7.4) Single muscle fiber Electrical pulse V (x, y, z),
Figure BDA0001226096410000081
wherein the electrical impulses between muscle fiber cells ei(z)；
7.5) Electrical impulses between muscle fiber cells ei(z)，
ei(z)＝96z3e-z-90 (5)
Wherein ei(z) is the electrical impulse between muscle fiber cells; z is the axial distance in millimeters; s is the fiber cross-section; sigmaiIs intercellular conductivity; sigmamIs muscle conductivity; r is the distance from the cross section of the fiber to the observation point;
7.6) muscle conductivity σmMuscle axial conductivity σzAnd muscle radial conductivity σy
Figure BDA0001226096410000082
7.7) according to Green's law, the electrical impulses for a single muscle fiber can be derived as in equation (7),
Figure BDA0001226096410000083
wherein S1, S2 are the sections of both ends of the muscle fiber, S is the muscle fiber section;
Figure BDA0001226096410000084
wherein d is the fiber diameter.
Step 8): storing and sending signals to an external controller;
step 9): the prosthetic hand/arm/foot/leg performs the specified action of the muscle output signal.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (2)

1. A simulation processing method for upper and lower limb movement is characterized in that: the method comprises the following steps:
step 1) selecting relevant muscles or muscle groups for completing specific actions;
step 2) extracting surface muscle signals by using a surface electrode, wherein the sampling frequency of the signals needs to be more than 1000Hz, and removing NaN signal values in sampling;
step 3): performing primary processing on the signals, eliminating hollow signal values, filtering the signals, and eliminating white noise, environmental electromagnetic signals and low-frequency signals generated by the movement of limbs of the user;
step 4): analyzing a frequency domain spectrum, filtering, and converting back to a time domain one-dimensional signal, wherein the range of the normal muscle electric signal is 8-500 Hz;
step 5): fitting a change curve of the contraction intensity of a single muscle in a designated exercise, muscle reaction time, contraction time and decay time;
step 6): correlation analysis and curve fitting of the above 4 parameters of two or more groups of muscles in the same designated action; the completion of each designated action is completed through the combined action of one or more groups of muscle groups, in the completion of the specific designated action, the starting reaction time, the contraction time and the decay time of each muscle of the muscle groups have specific changes and characteristics, the correlation and the fitting curve of muscle signals are obtained through extracting the related parameters, the reaction time, the contraction time and the decay time and through curve fitting and a signal correlation algorithm, and input parameters are provided for the next step of simulation modeling;
step 7): simulation modeling is carried out on the electromyographic signals of all muscle groups with specified actions and the relevant time and strength relation; the simulation modeling process is as follows:
the electrical muscle surface signals are composed of electrical impulses generated by a plurality of muscle motor unit groups, the electrical impulses generated by the motor unit groups are composed of countless electrical impulses of single muscle fibers in each unit group, wherein,
7.1) electric pulses V generated by a plurality of groups of locomotor unitsmui，
Figure FDA0002419665990000011
Wherein Vmui can be derived from equation 2; i is 1 to n, and Vmuti is the number of electric pulses V generated by each single motion unit groupmu，friIs the rate of motion unit release and follows a poisson distribution;
7.2) electric pulses V generated by a single group of locomotor unitsmu，
Figure FDA0002419665990000021
Wherein the content of the first and second substances,
Figure FDA0002419665990000022
is based on a single electrical pulse of muscle fibers in a given motor unit; n is a radical offIs the muscle fiber number;
7.3) muscle contraction time t for each group of motor unitsstart_timing，
Figure FDA0002419665990000023
Wherein N is 1,2mu；NmuIs based on the number of motor units a given muscle contains; fr is the median rate of release of the motor unit, and the default value is fr is 85 ms;
7.4) Single muscle fiber Electrical pulse V (x, y, z),
Figure FDA0002419665990000024
wherein the electrical impulses between muscle fiber cells ei(z)；
7.5) Electrical impulses between muscle fiber cells ei(z)，
ei(z)＝96z3e-z-90 (5)
Wherein ei(z) is the electrical impulse between muscle fiber cells; z is the axial distance in millimeters; s is the fiber cross-section; sigmaiIs intercellular conductivity; sigmamIs muscle conductivity; r is the distance from the cross section of the fiber to the observation point;
7.6) muscle conductivity σmMuscle axial conductivity σzAnd muscle radial conductivity σy
Figure FDA0002419665990000031
7.7) according to Green's law, the electrical impulses for a single muscle fiber can be derived as in equation (7),
Figure FDA0002419665990000032
wherein S1, S2 are the sections of both ends of the muscle fiber, S is the muscle fiber section;
Figure FDA0002419665990000033
wherein d is the fiber diameter;
step 8): storing and sending muscle signals to an external controller;
step 9): the prosthetic hand/arm/foot/leg performs the specified action of the muscle output signal.
2. A simulation processing method of upper and lower limb movement according to claim 1, characterized in that: the step 3) comprises the following specific steps: a large amount of external environment and low-frequency signals generated by self movement are mixed in muscle signals extracted from the surface of the skin through a surface electrode, the extracted surface muscle electrical signals are subjected to spectrum analysis, time domain one-dimensional digital signals are converted into frequency domains of the time domain one-dimensional digital signals, so that spectrum information of signals generated by various signal sources is obtained, in the frequency domain signals of the frequency spectrum of 0Hz-500Hz, environmental noise, the movement low-frequency signals and interference signals generated by deep muscles are eliminated through low-pass and band-pass filters, and the signal frequency spectrum finally used for simulation output falls between 8Hz-500Hz and is converted back to a time domain signal space.
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